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  3. Introduction to Python & Machine Learning (with Analytics Vidhya Hackathons)
  • 1

    Introduction to Python for Data Analysis

    This chapter will get you started with Python for Data Analysis. We will cover the reasons to learn Data Science using Python, provide an overview of the Python ecosystem and get you to write your first code in Python!

  • 2

    Python Libraries and data structures

    In this chapter, we will introduce some of the most common data structures in Python to you and take you through some of the libraries we commonly use in data analysis.

  • 3

    Exploratory analysis in Python using Pandas

    We start with the first step of data analysis - the exploratory data analysis.

  • 4

    Data Munging in Python using Pandas

    Pandas is at the heart of data analysis in Python. This chapter gets you started with Data Munging in Python using Pandas

  • 5

    Building a Predictive model in Python

    We build our predictive models and make submissions to the AV DataHack platform in this section.

  • 6

    Expert advice to improve model performance

    This chapter will help to understand the approach of data science experts, "How they do approach a challenge?", "How to select a right algorithm?", "How to combine outputs of multiple algorithms?" and "How to select the right value of model parameter also known as parameter tuning?".

Initializing

Exercise

Imputing missing values of LoanAmount

There are multiple ways to fill the missing values of continuous variables. You can replace them with mean, median or estimate values based on other features of the data set.

For the sake of simplicity, we would impute the missing values of LoanAmount by mean value (Mean of available values of LoanAmount).

train['LoanAmount'].fillna(train['LoanAmount'].mean(), inplace=True)

Instructions

100 XP

Impute missing values with a specific value 168